Importing the Libraries¶
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from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
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import warnings
warnings.filterwarnings('ignore')
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import numpy as np
import pandas as pd
import os
import shutil as sh
from IPython.display import Image
import torch
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import torch
from IPython.display import Image
import shutil
import os
from random import choice
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!unzip "/content/drive/MyDrive/4/acdcfogg.v3i.yolov5pytorch.zip" -d "/content/drive/MyDrive/4/data"
Archive: /content/drive/MyDrive/4/acdcfogg.v3i.yolov5pytorch.zip extracting: /content/drive/MyDrive/4/data/README.dataset.txt extracting: /content/drive/MyDrive/4/data/README.roboflow.txt extracting: /content/drive/MyDrive/4/data/data.yaml creating: /content/drive/MyDrive/4/data/test/ creating: /content/drive/MyDrive/4/data/test/images/ extracting: /content/drive/MyDrive/4/data/test/images/GOPR0475_frame_000300_rgb_anon_png.rf.bfa6bd4a07fa77a7f8112d6236838651.jpg extracting: /content/drive/MyDrive/4/data/test/images/GOPR0475_frame_000508_rgb_anon_png.rf.f12728dc4a653ea58b0fc8cdc78bd2f8.jpg extracting: /content/drive/MyDrive/4/data/test/images/GOPR0475_frame_000550_rgb_anon_png.rf.fe1a6585036a859204d3642788a14725.jpg extracting: /content/drive/MyDrive/4/data/test/images/GOPR0475_frame_000606_rgb_anon_png.rf.169c0e9dc84954e2c9a95832a6dd6cb3.jpg extracting: /content/drive/MyDrive/4/data/test/images/GOPR0475_frame_000670_rgb_anon_png.rf.355a22ce18b33c65d907cab1cd54ce34.jpg extracting: 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Cloning YoloV5 git Repo¶
In [5]:
!git clone https://github.com/ultralytics/yolov5
Cloning into 'yolov5'... remote: Enumerating objects: 16114, done. remote: Counting objects: 100% (25/25), done. remote: Compressing objects: 100% (24/24), done. remote: Total 16114 (delta 8), reused 10 (delta 1), pack-reused 16089 Receiving objects: 100% (16114/16114), 14.79 MiB | 21.15 MiB/s, done. Resolving deltas: 100% (11032/11032), done.
In [6]:
%cd /content/yolov5/
/content/yolov5
Installing YoloV5 requirements files¶
In [7]:
!pip install -r requirements.txt
Collecting gitpython>=3.1.30 (from -r requirements.txt (line 5))
Downloading GitPython-3.1.40-py3-none-any.whl (190 kB)
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Requirement already satisfied: matplotlib>=3.3 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 6)) (3.7.1)
Requirement already satisfied: numpy>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (1.23.5)
Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 8)) (4.8.0.76)
Collecting Pillow>=10.0.1 (from -r requirements.txt (line 9))
Downloading Pillow-10.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.6 MB)
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Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 10)) (5.9.5)
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Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 13)) (1.11.4)
Collecting thop>=0.1.1 (from -r requirements.txt (line 14))
Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)
Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 15)) (2.1.0+cu121)
Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 16)) (0.16.0+cu121)
Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 17)) (4.66.1)
Collecting ultralytics>=8.0.147 (from -r requirements.txt (line 18))
Downloading ultralytics-8.0.229-py3-none-any.whl (663 kB)
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Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 27)) (1.5.3)
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Requirement already satisfied: setuptools>=65.5.1 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 42)) (67.7.2)
Collecting gitdb<5,>=4.0.1 (from gitpython>=3.1.30->-r requirements.txt (line 5))
Downloading gitdb-4.0.11-py3-none-any.whl (62 kB)
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Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3->-r requirements.txt (line 6)) (1.2.0)
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Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 12)) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 12)) (3.6)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 12)) (2.0.7)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->-r requirements.txt (line 12)) (2023.11.17)
Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (3.13.1)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (4.5.0)
Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (1.12)
Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (3.2.1)
Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (3.1.2)
Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (2023.6.0)
Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->-r requirements.txt (line 15)) (2.1.0)
Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics>=8.0.147->-r requirements.txt (line 18)) (9.0.0)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->-r requirements.txt (line 27)) (2023.3.post1)
Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython>=3.1.30->-r requirements.txt (line 5))
Downloading smmap-5.0.1-py3-none-any.whl (24 kB)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3->-r requirements.txt (line 6)) (1.16.0)
Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->-r requirements.txt (line 15)) (2.1.3)
Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->-r requirements.txt (line 15)) (1.3.0)
Installing collected packages: smmap, Pillow, gitdb, thop, gitpython, ultralytics
Attempting uninstall: Pillow
Found existing installation: Pillow 9.4.0
Uninstalling Pillow-9.4.0:
Successfully uninstalled Pillow-9.4.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.1.0 which is incompatible.
Successfully installed Pillow-10.1.0 gitdb-4.0.11 gitpython-3.1.40 smmap-5.0.1 thop-0.1.1.post2209072238 ultralytics-8.0.229
Downloading dataset.yaml file from google cloud for the dataset¶
In [8]:
with open(r'/content/drive/MyDrive/4/data/data.yaml', 'r') as file:
lines = file.readlines()
print(lines)
['train: /content/drive/MyDrive/4/data/train/images\n', 'val: /content/drive/MyDrive/4/data/valid/images\n', '\n', '\n', 'nc: 10\n', "names: ['0- person', '1- bicycle', '2- car', '3- motorcycle', '4- airplane', '5- bus', '6- train', '7- truck', '8- boat', '9- traffic light']\n", '\n', 'roboflow:\n', ' workspace: debasiskumaredugmailcom-oadi4\n', ' project: acdcfogg\n', ' version: 3\n', ' license: CC BY 4.0\n', ' url: https://universe.roboflow.com/debasiskumaredugmailcom-oadi4/acdcfogg/dataset/3']
In [2]:
ML_Model = []
precision = []
recall = []
mAP = []
#function to call for storing the results
def storeResults(model, a,b,c):
ML_Model.append(model)
precision.append(round(a, 3))
recall.append(round(b, 3))
mAP.append(round(c, 3))
Yolo V5x6¶
In [9]:
!wandb disabled
!python train.py --img 415 --batch 2 --epochs 50 --data /content/drive/MyDrive/4/data/data.yaml --weights yolov5x6.pt --cache --workers 2
/bin/bash: line 1: wandb: command not found 2023-12-26 18:52:53.559496: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2023-12-26 18:52:53.559539: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2023-12-26 18:52:53.560946: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered train: weights=yolov5x6.pt, cfg=, data=/content/drive/MyDrive/4/data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=2, imgsz=415, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=2, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-253-g63555c8 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf... 100% 755k/755k [00:00<00:00, 4.86MB/s] Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt to yolov5x6.pt... 100% 270M/270M [00:03<00:00, 75.9MB/s] Overriding model.yaml nc=80 with nc=10 from n params module arguments 0 -1 1 8800 models.common.Conv [3, 80, 6, 2, 2] 1 -1 1 115520 models.common.Conv [80, 160, 3, 2] 2 -1 4 309120 models.common.C3 [160, 160, 4] 3 -1 1 461440 models.common.Conv [160, 320, 3, 2] 4 -1 8 2259200 models.common.C3 [320, 320, 8] 5 -1 1 1844480 models.common.Conv [320, 640, 3, 2] 6 -1 12 13125120 models.common.C3 [640, 640, 12] 7 -1 1 5531520 models.common.Conv [640, 960, 3, 2] 8 -1 4 11070720 models.common.C3 [960, 960, 4] 9 -1 1 11061760 models.common.Conv [960, 1280, 3, 2] 10 -1 4 19676160 models.common.C3 [1280, 1280, 4] 11 -1 1 4099840 models.common.SPPF [1280, 1280, 5] 12 -1 1 1230720 models.common.Conv [1280, 960, 1, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 8] 1 0 models.common.Concat [1] 15 -1 4 11992320 models.common.C3 [1920, 960, 4, False] 16 -1 1 615680 models.common.Conv [960, 640, 1, 1] 17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 18 [-1, 6] 1 0 models.common.Concat [1] 19 -1 4 5332480 models.common.C3 [1280, 640, 4, False] 20 -1 1 205440 models.common.Conv [640, 320, 1, 1] 21 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 22 [-1, 4] 1 0 models.common.Concat [1] 23 -1 4 1335040 models.common.C3 [640, 320, 4, False] 24 -1 1 922240 models.common.Conv [320, 320, 3, 2] 25 [-1, 20] 1 0 models.common.Concat [1] 26 -1 4 4922880 models.common.C3 [640, 640, 4, False] 27 -1 1 3687680 models.common.Conv [640, 640, 3, 2] 28 [-1, 16] 1 0 models.common.Concat [1] 29 -1 4 11377920 models.common.C3 [1280, 960, 4, False] 30 -1 1 8296320 models.common.Conv [960, 960, 3, 2] 31 [-1, 12] 1 0 models.common.Concat [1] 32 -1 4 20495360 models.common.C3 [1920, 1280, 4, False] 33 [23, 26, 29, 32] 1 144180 models.yolo.Detect [10, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [320, 640, 960, 1280]] Model summary: 575 layers, 140121940 parameters, 140121940 gradients, 208.9 GFLOPs Transferred 955/963 items from yolov5x6.pt AMP: checks passed ✅ WARNING ⚠️ --img-size 415 must be multiple of max stride 64, updating to 448 optimizer: SGD(lr=0.01) with parameter groups 159 weight(decay=0.0), 163 weight(decay=0.0005), 163 bias albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) train: Scanning /content/drive/MyDrive/4/data/train/labels.cache... 639 images, 0 backgrounds, 0 corrupt: 100% 639/639 [00:00<?, ?it/s] train: Caching images (0.4GB ram): 100% 639/639 [00:15<00:00, 41.49it/s] val: Scanning /content/drive/MyDrive/4/data/valid/labels.cache... 180 images, 0 backgrounds, 0 corrupt: 100% 180/180 [00:00<?, ?it/s] val: Caching images (0.1GB ram): 100% 180/180 [00:01<00:00, 151.84it/s] AutoAnchor: 2.40 anchors/target, 0.947 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve... AutoAnchor: WARNING ⚠️ Extremely small objects found: 14 of 3765 labels are <3 pixels in size AutoAnchor: Running kmeans for 12 anchors on 3765 points... AutoAnchor: Evolving anchors with Genetic Algorithm: fitness = 0.7979: 100% 1000/1000 [00:00<00:00, 1382.52it/s] AutoAnchor: thr=0.25: 1.0000 best possible recall, 6.96 anchors past thr AutoAnchor: n=12, img_size=448, metric_all=0.348/0.798-mean/best, past_thr=0.507-mean: 6,10, 10,14, 6,22, 13,19, 11,37, 20,26, 29,37, 47,45, 33,109, 74,81, 109,204, 162,283 AutoAnchor: Done ✅ (optional: update model *.yaml to use these anchors in the future) Plotting labels to runs/train/exp/labels.jpg... Image sizes 448 train, 448 val Using 2 dataloader workers Logging results to runs/train/exp Starting training for 50 epochs... Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0/49 3.04G 0.1397 0.01413 0.06229 7 448: 0% 0/320 [00:01<?, ?it/s]WARNING ⚠️ TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 14 but got size 13 for tensor number 1 in the list. 0/49 3.47G 0.1246 0.02446 0.04736 15 448: 100% 320/320 [00:52<00:00, 6.04it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:05<00:00, 7.69it/s] all 180 1126 0.471 0.0262 0.0187 0.00452 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 1/49 3.47G 0.1044 0.02223 0.03009 15 448: 100% 320/320 [00:47<00:00, 6.77it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:05<00:00, 8.00it/s] all 180 1126 0.365 0.0861 0.0263 0.00798 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 2/49 3.47G 0.1025 0.02073 0.02578 4 448: 100% 320/320 [00:46<00:00, 6.82it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 9.91it/s] all 180 1126 0.665 0.0954 0.0858 0.0275 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 3/49 3.47G 0.09848 0.01801 0.02358 5 448: 100% 320/320 [00:46<00:00, 6.89it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.86it/s] all 180 1126 0.701 0.187 0.154 0.0486 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 4/49 3.47G 0.09398 0.02022 0.02153 7 448: 100% 320/320 [00:46<00:00, 6.85it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.82it/s] all 180 1126 0.74 0.175 0.228 0.0718 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 5/49 3.47G 0.0877 0.01914 0.02003 0 448: 100% 320/320 [00:46<00:00, 6.87it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.78it/s] all 180 1126 0.781 0.183 0.255 0.0982 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 6/49 3.47G 0.08417 0.01949 0.01925 10 448: 100% 320/320 [00:46<00:00, 6.90it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.06it/s] all 180 1126 0.629 0.277 0.301 0.137 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 7/49 3.47G 0.07982 0.0191 0.01833 9 448: 100% 320/320 [00:46<00:00, 6.88it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.68it/s] all 180 1126 0.584 0.365 0.321 0.15 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 8/49 3.47G 0.08167 0.01952 0.01782 12 448: 100% 320/320 [00:46<00:00, 6.83it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.05it/s] all 180 1126 0.587 0.353 0.289 0.124 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 9/49 3.47G 0.07757 0.01872 0.01641 4 448: 100% 320/320 [00:46<00:00, 6.83it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.93it/s] all 180 1126 0.577 0.368 0.321 0.167 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 10/49 3.47G 0.07754 0.01975 0.01701 19 448: 100% 320/320 [00:46<00:00, 6.86it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.95it/s] all 180 1126 0.664 0.361 0.382 0.192 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 11/49 3.47G 0.07353 0.01867 0.01567 6 448: 100% 320/320 [00:46<00:00, 6.87it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.87it/s] all 180 1126 0.614 0.408 0.348 0.181 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 12/49 3.47G 0.07327 0.01853 0.0154 1 448: 100% 320/320 [00:46<00:00, 6.87it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.90it/s] all 180 1126 0.673 0.355 0.339 0.168 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 13/49 3.47G 0.07238 0.01842 0.01511 12 448: 100% 320/320 [00:46<00:00, 6.86it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.01it/s] all 180 1126 0.687 0.357 0.35 0.155 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 14/49 3.47G 0.07046 0.01843 0.01471 0 448: 100% 320/320 [00:46<00:00, 6.87it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.01it/s] all 180 1126 0.639 0.41 0.365 0.183 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 15/49 3.47G 0.07065 0.01917 0.01374 21 448: 100% 320/320 [00:46<00:00, 6.86it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.04it/s] all 180 1126 0.733 0.361 0.375 0.177 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 16/49 3.47G 0.07063 0.01909 0.01421 0 448: 100% 320/320 [00:46<00:00, 6.87it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.84it/s] all 180 1126 0.667 0.425 0.427 0.215 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 17/49 3.47G 0.06915 0.01917 0.01404 8 448: 100% 320/320 [00:47<00:00, 6.77it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.83it/s] all 180 1126 0.61 0.393 0.418 0.205 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 18/49 3.47G 0.06882 0.01896 0.01429 3 448: 100% 320/320 [00:46<00:00, 6.83it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.75it/s] all 180 1126 0.57 0.501 0.435 0.209 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 19/49 3.47G 0.06781 0.01814 0.01368 11 448: 100% 320/320 [00:46<00:00, 6.87it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.97it/s] all 180 1126 0.602 0.439 0.392 0.188 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 20/49 3.47G 0.06729 0.01939 0.01319 8 448: 100% 320/320 [00:46<00:00, 6.94it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.08it/s] all 180 1126 0.562 0.476 0.431 0.208 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 21/49 3.47G 0.06693 0.01918 0.01264 18 448: 100% 320/320 [00:46<00:00, 6.95it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.88it/s] all 180 1126 0.581 0.443 0.441 0.228 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 22/49 3.47G 0.06558 0.01846 0.01335 0 448: 100% 320/320 [00:45<00:00, 7.00it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.06it/s] all 180 1126 0.622 0.441 0.546 0.306 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 23/49 3.47G 0.06409 0.01785 0.01272 8 448: 100% 320/320 [00:46<00:00, 6.89it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.08it/s] all 180 1126 0.649 0.46 0.473 0.25 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 24/49 3.47G 0.0632 0.0169 0.01193 5 448: 100% 320/320 [00:46<00:00, 6.95it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.03it/s] all 180 1126 0.605 0.454 0.464 0.251 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 25/49 3.47G 0.06442 0.01799 0.01161 4 448: 100% 320/320 [00:46<00:00, 6.89it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.13it/s] all 180 1126 0.474 0.46 0.558 0.291 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 26/49 3.47G 0.06397 0.01823 0.01252 11 448: 100% 320/320 [00:46<00:00, 6.92it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.03it/s] all 180 1126 0.464 0.535 0.577 0.291 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 27/49 3.47G 0.06279 0.01831 0.01313 6 448: 100% 320/320 [00:46<00:00, 6.91it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.06it/s] all 180 1126 0.69 0.452 0.588 0.295 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 28/49 3.47G 0.0618 0.01777 0.01147 6 448: 100% 320/320 [00:46<00:00, 6.91it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.92it/s] all 180 1126 0.583 0.431 0.578 0.269 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 29/49 3.47G 0.06197 0.01749 0.01246 1 448: 100% 320/320 [00:46<00:00, 6.89it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.95it/s] all 180 1126 0.557 0.461 0.602 0.281 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 30/49 3.47G 0.06191 0.0176 0.01158 4 448: 100% 320/320 [00:46<00:00, 6.92it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.85it/s] all 180 1126 0.551 0.445 0.575 0.317 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 31/49 3.47G 0.06015 0.01725 0.01106 4 448: 100% 320/320 [00:46<00:00, 6.92it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.06it/s] all 180 1126 0.506 0.511 0.589 0.307 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 32/49 3.47G 0.06173 0.01794 0.01121 2 448: 100% 320/320 [00:46<00:00, 6.86it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.92it/s] all 180 1126 0.541 0.488 0.595 0.312 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 33/49 3.47G 0.05964 0.01781 0.0103 2 448: 100% 320/320 [00:46<00:00, 6.92it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.02it/s] all 180 1126 0.557 0.475 0.601 0.301 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 34/49 3.47G 0.05971 0.01766 0.01088 29 448: 100% 320/320 [00:46<00:00, 6.93it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.95it/s] all 180 1126 0.715 0.453 0.589 0.291 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 35/49 3.47G 0.05909 0.01675 0.01072 5 448: 100% 320/320 [00:46<00:00, 6.92it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.96it/s] all 180 1126 0.472 0.592 0.602 0.316 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 36/49 3.47G 0.05738 0.01626 0.01038 22 448: 100% 320/320 [00:46<00:00, 6.95it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.07it/s] all 180 1126 0.573 0.446 0.479 0.26 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 37/49 3.47G 0.05761 0.01615 0.01031 6 448: 100% 320/320 [00:46<00:00, 6.96it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.12it/s] all 180 1126 0.508 0.572 0.568 0.275 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 38/49 3.47G 0.05752 0.01692 0.01025 2 448: 100% 320/320 [00:46<00:00, 6.89it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.17it/s] all 180 1126 0.531 0.545 0.514 0.275 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 39/49 3.47G 0.05627 0.01746 0.009911 7 448: 100% 320/320 [00:46<00:00, 6.94it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.07it/s] all 180 1126 0.586 0.484 0.579 0.291 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 40/49 3.47G 0.0562 0.01682 0.009725 11 448: 100% 320/320 [00:46<00:00, 6.94it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.12it/s] all 180 1126 0.556 0.467 0.462 0.272 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 41/49 3.47G 0.05551 0.01618 0.009808 11 448: 100% 320/320 [00:46<00:00, 6.95it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.08it/s] all 180 1126 0.535 0.474 0.487 0.297 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 42/49 3.47G 0.05432 0.01603 0.009123 1 448: 100% 320/320 [00:45<00:00, 6.99it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.15it/s] all 180 1126 0.541 0.492 0.49 0.297 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 43/49 3.47G 0.05584 0.01739 0.01002 9 448: 100% 320/320 [00:45<00:00, 6.98it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.98it/s] all 180 1126 0.566 0.481 0.607 0.326 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 44/49 3.47G 0.05339 0.016 0.009181 5 448: 100% 320/320 [00:45<00:00, 6.98it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.10it/s] all 180 1126 0.507 0.589 0.621 0.345 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 45/49 3.47G 0.0535 0.01637 0.008811 0 448: 100% 320/320 [00:46<00:00, 6.92it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.06it/s] all 180 1126 0.568 0.484 0.619 0.349 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 46/49 3.47G 0.05386 0.01648 0.009426 13 448: 100% 320/320 [00:46<00:00, 6.90it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.85it/s] all 180 1126 0.576 0.495 0.624 0.362 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 47/49 3.47G 0.05389 0.01617 0.009545 6 448: 100% 320/320 [00:46<00:00, 6.82it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.99it/s] all 180 1126 0.534 0.577 0.628 0.375 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 48/49 3.47G 0.05405 0.01646 0.009282 3 448: 100% 320/320 [00:46<00:00, 6.84it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 11.00it/s] all 180 1126 0.532 0.551 0.625 0.373 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 49/49 3.47G 0.05255 0.01642 0.00865 10 448: 100% 320/320 [00:46<00:00, 6.89it/s] Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.96it/s] all 180 1126 0.591 0.462 0.62 0.356 50 epochs completed in 0.766 hours. Optimizer stripped from runs/train/exp/weights/last.pt, 281.0MB Optimizer stripped from runs/train/exp/weights/best.pt, 281.0MB Validating runs/train/exp/weights/best.pt... Fusing layers... Model summary: 416 layers, 140057380 parameters, 0 gradients, 208.1 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100% 45/45 [00:04<00:00, 10.06it/s] all 180 1126 0.535 0.576 0.628 0.375 0- person 180 115 0.565 0.565 0.57 0.305 1- bicycle 180 2 0.075 0.5 0.495 0.347 2- car 180 751 0.651 0.832 0.831 0.532 3- motorcycle 180 6 0.336 0.5 0.241 0.123 5- bus 180 6 0.246 0.5 0.373 0.328 6- train 180 8 0.661 0.732 0.642 0.496 7- truck 180 97 0.617 0.773 0.727 0.495 8- boat 180 1 1 0 0.995 0.398 9- traffic light 180 140 0.661 0.786 0.775 0.352 Results saved to runs/train/exp
In [3]:
p_yol5 = 0.535
r_yol5 = 0.576
mAP_yolo5 = 0.628
storeResults('YoloV5x6',p_yol5,r_yol5,mAP_yolo5)
In [10]:
# dislaying metrics for train data
from IPython.display import Image
from IPython.display import display
x = Image(filename='runs/train/exp/F1_curve.png')
y = Image(filename='runs/train/exp/PR_curve.png')
z = Image(filename='runs/train/exp/confusion_matrix.png')
display(x, y,z)
Yolo v4
In [11]:
!wandb disabled
!python train.py --img 415 --batch 16 --epochs 50 --data /content/drive/MyDrive/4/data/data.yaml --weights '' --cfg /content/drive/MyDrive/4/yolov4.yaml --cache --workers 2
/bin/bash: line 1: wandb: command not found 2023-12-26 19:42:06.458526: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2023-12-26 19:42:06.458579: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2023-12-26 19:42:06.460028: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered train: weights=, cfg=/content/drive/MyDrive/4/yolov4.yaml, data=/content/drive/MyDrive/4/data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=415, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=2, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-253-g63555c8 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Overriding model.yaml nc=80 with nc=10 from n params module arguments 0 -1 1 928 models.common.Conv [3, 32, 3, 1] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 20672 models.common.Bottleneck [64, 64] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 164608 models.common.Bottleneck [128, 128] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 8 2627584 models.common.Bottleneck [256, 256] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 8 10498048 models.common.Bottleneck [512, 512] 9 -1 1 4720640 models.common.Conv [512, 1024, 3, 2] 10 -1 4 20983808 models.common.Bottleneck [1024, 1024] 11 -1 1 5245952 models.common.Bottleneck [1024, 1024, False] 12 -1 1 1574912 models.common.SPP [1024, 512, [5, 9, 13]] 13 -1 1 4720640 models.common.Conv [512, 1024, 3, 1] 14 -1 1 525312 models.common.Conv [1024, 512, 1, 1] 15 -1 1 4720640 models.common.Conv [512, 1024, 3, 1] 16 -2 1 131584 models.common.Conv [512, 256, 1, 1] 17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 18 [-1, 8] 1 0 models.common.Concat [1] 19 -1 1 1377792 models.common.Bottleneck [768, 512, False] 20 -1 1 1312256 models.common.Bottleneck [512, 512, False] 21 -1 1 131584 models.common.Conv [512, 256, 1, 1] 22 -1 1 1180672 models.common.Conv [256, 512, 3, 1] 23 -2 1 33024 models.common.Conv [256, 128, 1, 1] 24 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 25 [-1, 6] 1 0 models.common.Concat [1] 26 -1 1 344832 models.common.Bottleneck [384, 256, False] 27 -1 2 656896 models.common.Bottleneck [256, 256, False] 28 [27, 22, 15] 1 80775 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]] yolov4 summary: 270 layers, 62621799 parameters, 62621799 gradients, 156.3 GFLOPs AMP: checks passed ✅ WARNING ⚠️ --img-size 415 must be multiple of max stride 32, updating to 416 optimizer: SGD(lr=0.01) with parameter groups 73 weight(decay=0.0), 76 weight(decay=0.0005), 76 bias albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) train: Scanning /content/drive/MyDrive/4/data/train/labels.cache... 639 images, 0 backgrounds, 0 corrupt: 100% 639/639 [00:00<?, ?it/s] train: Caching images (0.3GB ram): 100% 639/639 [00:02<00:00, 248.75it/s] val: Scanning /content/drive/MyDrive/4/data/valid/labels.cache... 180 images, 0 backgrounds, 0 corrupt: 100% 180/180 [00:00<?, ?it/s] val: Caching images (0.1GB ram): 100% 180/180 [00:01<00:00, 153.58it/s] AutoAnchor: 3.75 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/exp2/labels.jpg... Image sizes 416 train, 416 val Using 2 dataloader workers Logging results to runs/train/exp2 Starting training for 50 epochs... Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0/49 6.05G 0.1127 0.03066 0.06205 141 416: 100% 40/40 [00:17<00:00, 2.27it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.42it/s] all 180 1126 0.000169 0.0105 9.36e-05 3.05e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 1/49 7.49G 0.1088 0.03056 0.05663 141 416: 100% 40/40 [00:14<00:00, 2.75it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.81it/s] all 180 1126 0.000184 0.0134 0.000112 2.84e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 2/49 7.49G 0.1078 0.03207 0.04927 98 416: 100% 40/40 [00:14<00:00, 2.72it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.70it/s] all 180 1126 0.000138 0.00991 9.23e-05 2.02e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 3/49 7.49G 0.1059 0.02851 0.04228 128 416: 100% 40/40 [00:14<00:00, 2.71it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:03<00:00, 1.80it/s] all 180 1126 0.000187 0.0135 0.000105 2.97e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 4/49 7.49G 0.1044 0.03074 0.03762 149 416: 100% 40/40 [00:14<00:00, 2.75it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:03<00:00, 1.61it/s] all 180 1126 0.000414 0.0297 0.000883 0.000209 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 5/49 7.49G 0.102 0.02775 0.03576 160 416: 100% 40/40 [00:14<00:00, 2.75it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:06<00:00, 1.16s/it] all 180 1126 0.00561 0.0613 0.00443 0.000981 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 6/49 7.49G 0.09918 0.02816 0.03413 128 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.35it/s] all 180 1126 0.69 0.026 0.0131 0.00403 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 7/49 7.49G 0.09593 0.02653 0.03309 100 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.44it/s] all 180 1126 0.795 0.0216 0.0103 0.00289 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 8/49 7.49G 0.09498 0.02678 0.03297 125 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.693 0.0254 0.0125 0.00397 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 9/49 7.49G 0.09267 0.02524 0.0308 103 416: 100% 40/40 [00:14<00:00, 2.75it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.38it/s] all 180 1126 0.803 0.0399 0.017 0.0051 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 10/49 7.49G 0.09139 0.02585 0.03006 113 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.701 0.0574 0.0308 0.00979 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 11/49 7.49G 0.0886 0.02533 0.02948 112 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.33it/s] all 180 1126 0.721 0.0774 0.0517 0.0174 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 12/49 7.49G 0.08719 0.02452 0.02902 107 416: 100% 40/40 [00:14<00:00, 2.72it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.44it/s] all 180 1126 0.731 0.0765 0.0534 0.0181 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 13/49 7.49G 0.08518 0.02453 0.02822 139 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.721 0.0682 0.0458 0.0152 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 14/49 7.49G 0.08492 0.0246 0.02916 91 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.30it/s] all 180 1126 0.735 0.0849 0.0652 0.0235 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 15/49 7.49G 0.08524 0.0244 0.02801 156 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.937 0.0559 0.0736 0.0287 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 16/49 7.49G 0.08353 0.0246 0.02658 123 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.41it/s] all 180 1126 0.868 0.0511 0.0571 0.0219 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 17/49 7.49G 0.08357 0.02397 0.02683 132 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.40it/s] all 180 1126 0.811 0.0937 0.0934 0.0365 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 18/49 7.49G 0.08108 0.02412 0.02699 106 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.38it/s] all 180 1126 0.816 0.0715 0.0791 0.0313 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 19/49 7.49G 0.0804 0.02373 0.02718 88 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.50it/s] all 180 1126 0.753 0.101 0.0792 0.0303 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 20/49 7.49G 0.07864 0.02506 0.02539 150 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.44it/s] all 180 1126 0.826 0.114 0.12 0.0464 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 21/49 7.49G 0.07742 0.02467 0.02443 160 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.725 0.104 0.105 0.0391 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 22/49 7.49G 0.07626 0.02347 0.0254 152 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.45it/s] all 180 1126 0.836 0.103 0.119 0.0492 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 23/49 7.49G 0.0749 0.02319 0.02327 136 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.803 0.123 0.127 0.0535 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 24/49 7.49G 0.07148 0.02202 0.02261 76 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.773 0.126 0.133 0.0505 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 25/49 7.49G 0.06953 0.02378 0.02182 131 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.45it/s] all 180 1126 0.841 0.128 0.15 0.0613 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 26/49 7.49G 0.06877 0.02239 0.02278 115 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.44it/s] all 180 1126 0.836 0.13 0.155 0.0647 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 27/49 7.49G 0.06814 0.02376 0.02358 112 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.835 0.12 0.14 0.055 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 28/49 7.49G 0.06578 0.02241 0.02252 142 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.46it/s] all 180 1126 0.835 0.137 0.168 0.0681 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 29/49 7.49G 0.06591 0.02234 0.02193 98 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.45it/s] all 180 1126 0.732 0.142 0.173 0.0714 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 30/49 7.49G 0.06571 0.02242 0.02164 108 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.868 0.138 0.175 0.0731 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 31/49 7.49G 0.06346 0.02263 0.02208 100 416: 100% 40/40 [00:14<00:00, 2.72it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.45it/s] all 180 1126 0.826 0.142 0.159 0.0684 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 32/49 7.49G 0.06293 0.02197 0.02055 79 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.854 0.141 0.17 0.0721 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 33/49 7.49G 0.06254 0.02246 0.02048 168 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.44it/s] all 180 1126 0.804 0.143 0.178 0.0779 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 34/49 7.49G 0.06122 0.02248 0.0193 154 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.757 0.16 0.178 0.0709 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 35/49 7.49G 0.06106 0.02122 0.01954 137 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.877 0.129 0.163 0.0662 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 36/49 7.49G 0.05973 0.02022 0.01958 120 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.809 0.156 0.192 0.0796 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 37/49 7.49G 0.05859 0.02087 0.02013 135 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.812 0.15 0.199 0.0819 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 38/49 7.49G 0.05928 0.02136 0.01903 129 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.828 0.165 0.201 0.0847 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 39/49 7.49G 0.05803 0.02209 0.01888 82 416: 100% 40/40 [00:14<00:00, 2.72it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.85 0.134 0.184 0.0785 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 40/49 7.49G 0.05817 0.02179 0.01867 149 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.847 0.134 0.194 0.0854 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 41/49 7.49G 0.05688 0.02091 0.01898 129 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.832 0.145 0.201 0.0891 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 42/49 7.49G 0.05671 0.02023 0.0185 83 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.44it/s] all 180 1126 0.862 0.16 0.217 0.0973 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 43/49 7.49G 0.05731 0.02209 0.0197 181 416: 100% 40/40 [00:14<00:00, 2.72it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.832 0.161 0.21 0.0939 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 44/49 7.49G 0.05552 0.02073 0.01812 100 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.81 0.164 0.207 0.0915 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 45/49 7.49G 0.05432 0.02069 0.01642 91 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.82 0.17 0.212 0.0966 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 46/49 7.49G 0.05465 0.02093 0.01794 149 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.47it/s] all 180 1126 0.811 0.17 0.216 0.094 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 47/49 7.49G 0.05482 0.02083 0.01822 172 416: 100% 40/40 [00:14<00:00, 2.74it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.793 0.187 0.246 0.114 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 48/49 7.49G 0.05416 0.02084 0.01756 124 416: 100% 40/40 [00:14<00:00, 2.69it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.49it/s] all 180 1126 0.798 0.188 0.25 0.115 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 49/49 7.49G 0.05368 0.02135 0.01664 95 416: 100% 40/40 [00:14<00:00, 2.73it/s] Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:02<00:00, 2.48it/s] all 180 1126 0.794 0.191 0.245 0.113 50 epochs completed in 0.267 hours. Optimizer stripped from runs/train/exp2/weights/last.pt, 125.6MB Optimizer stripped from runs/train/exp2/weights/best.pt, 125.6MB Validating runs/train/exp2/weights/best.pt... Fusing layers... yolov4 summary: 197 layers, 62594983 parameters, 0 gradients, 155.5 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100% 6/6 [00:03<00:00, 1.58it/s] all 180 1126 0.799 0.187 0.251 0.115 0- person 180 115 0.625 0.139 0.202 0.0703 1- bicycle 180 2 1 0 0.0375 0.00978 2- car 180 751 0.651 0.652 0.676 0.342 3- motorcycle 180 6 1 0 0.0231 0.0119 5- bus 180 6 1 0 0.0143 0.00713 6- train 180 8 1 0 0.421 0.214 7- truck 180 97 0.407 0.485 0.462 0.221 8- boat 180 1 1 0 0 0 9- traffic light 180 140 0.509 0.407 0.419 0.16 Results saved to runs/train/exp2
In [4]:
p_yol5 = 0.799
r_yol5 = 0.187
mAP_yolo5 = 0.256
storeResults('YoloV4',p_yol5,r_yol5,mAP_yolo5)
In [12]:
# dislaying metrics for train data
from IPython.display import Image
from IPython.display import display
x = Image(filename='runs/train/exp2/F1_curve.png')
y = Image(filename='runs/train/exp2/PR_curve.png')
z = Image(filename='runs/train/exp2/confusion_matrix.png')
display(x, y,z)
In [18]:
!zip -r /content/drive/MyDrive/4/file.zip /content/yolov5
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!git clone https://github.com/SunetK/MCD-YOLOv5-joint-DPC
Cloning into 'MCD-YOLOv5-joint-DPC'... remote: Enumerating objects: 329, done. remote: Counting objects: 100% (182/182), done. remote: Compressing objects: 100% (154/154), done. remote: Total 329 (delta 75), reused 82 (delta 20), pack-reused 147 Receiving objects: 100% (329/329), 338.64 KiB | 2.29 MiB/s, done. Resolving deltas: 100% (111/111), done.
MC-DAYolo
In [16]:
!wandb disabled
!python train.py --img 415 --batch 4 --epochs 50 --data /content/drive/MyDrive/4/data/data.yaml --weights '' --cfg /content/yolov5/MCD-YOLOv5-joint-DPC/MCD-YOLOv5/models/yolov5x.yaml --cache --workers 2
/bin/bash: line 1: wandb: command not found 2023-12-26 20:03:24.803182: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2023-12-26 20:03:24.803236: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2023-12-26 20:03:24.804723: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered train: weights=, cfg=/content/yolov5/MCD-YOLOv5-joint-DPC/MCD-YOLOv5/models/yolov5x.yaml, data=/content/drive/MyDrive/4/data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=4, imgsz=415, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=2, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: up to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-253-g63555c8 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0 Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Overriding model.yaml nc=80 with nc=10 from n params module arguments 0 -1 1 8800 models.common.Conv [3, 80, 6, 2, 2] 1 -1 1 115520 models.common.Conv [80, 160, 3, 2] 2 -1 4 309120 models.common.C3 [160, 160, 4] 3 -1 1 461440 models.common.Conv [160, 320, 3, 2] 4 -1 8 2259200 models.common.C3 [320, 320, 8] 5 -1 1 1844480 models.common.Conv [320, 640, 3, 2] 6 -1 12 13125120 models.common.C3 [640, 640, 12] 7 -1 1 7375360 models.common.Conv [640, 1280, 3, 2] 8 -1 4 19676160 models.common.C3 [1280, 1280, 4] 9 -1 1 4099840 models.common.SPPF [1280, 1280, 5] 10 -1 1 820480 models.common.Conv [1280, 640, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 4 5332480 models.common.C3 [1280, 640, 4, False] 14 -1 1 205440 models.common.Conv [640, 320, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 4 1335040 models.common.C3 [640, 320, 4, False] 18 -1 1 922240 models.common.Conv [320, 320, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 4 4922880 models.common.C3 [640, 640, 4, False] 21 -1 1 3687680 models.common.Conv [640, 640, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 4 19676160 models.common.C3 [1280, 1280, 4, False] 24 [17, 20, 23] 1 100935 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [320, 640, 1280]] YOLOv5x summary: 445 layers, 86278375 parameters, 86278375 gradients, 204.8 GFLOPs AMP: checks passed ✅ WARNING ⚠️ --img-size 415 must be multiple of max stride 32, updating to 416 optimizer: SGD(lr=0.01) with parameter groups 123 weight(decay=0.0), 126 weight(decay=0.0005), 126 bias albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8)) train: Scanning /content/drive/MyDrive/4/data/train/labels.cache... 639 images, 0 backgrounds, 0 corrupt: 100% 639/639 [00:00<?, ?it/s] train: Caching images (0.3GB ram): 100% 639/639 [00:02<00:00, 236.75it/s] val: Scanning /content/drive/MyDrive/4/data/valid/labels.cache... 180 images, 0 backgrounds, 0 corrupt: 100% 180/180 [00:00<?, ?it/s] val: Caching images (0.1GB ram): 100% 180/180 [00:01<00:00, 154.65it/s] AutoAnchor: 3.75 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/exp3/labels.jpg... Image sizes 416 train, 416 val Using 2 dataloader workers Logging results to runs/train/exp3 Starting training for 50 epochs... Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0/49 3.16G 0.1157 0.02883 0.06291 29 416: 100% 160/160 [00:31<00:00, 5.06it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.72it/s] all 180 1126 0.000187 0.0284 0.000113 3.12e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 1/49 3.16G 0.1146 0.02853 0.06257 26 416: 100% 160/160 [00:26<00:00, 6.09it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:08<00:00, 2.72it/s] all 180 1126 6.34e-06 0.000148 3.1e-06 6.19e-07 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 2/49 3.16G 0.1119 0.03031 0.06181 21 416: 100% 160/160 [00:26<00:00, 6.00it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:06<00:00, 3.83it/s] all 180 1126 8.76e-05 0.00355 4.67e-05 1.77e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 3/49 3.16G 0.1088 0.02845 0.05963 35 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.44it/s] all 180 1126 0.000206 0.0136 0.000112 3.55e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 4/49 3.16G 0.1073 0.03272 0.05556 23 416: 100% 160/160 [00:26<00:00, 6.09it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:02<00:00, 8.01it/s] all 180 1126 0.000124 0.00888 7.91e-05 2.16e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 5/49 3.16G 0.1063 0.02969 0.04911 29 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:02<00:00, 7.89it/s] all 180 1126 0.000113 0.00799 6.98e-05 2.09e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 6/49 3.16G 0.1061 0.0308 0.04267 47 416: 100% 160/160 [00:26<00:00, 6.13it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:02<00:00, 7.76it/s] all 180 1126 0.00015 0.0108 9.89e-05 2.39e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 7/49 3.16G 0.1044 0.02996 0.03713 12 416: 100% 160/160 [00:26<00:00, 6.12it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.61it/s] all 180 1126 0.000171 0.0123 9.91e-05 2.31e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 8/49 3.16G 0.1054 0.03025 0.03627 21 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.61it/s] all 180 1126 0.000214 0.0154 0.000163 4.38e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 9/49 3.16G 0.1046 0.02929 0.03454 7 416: 100% 160/160 [00:26<00:00, 6.11it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.71it/s] all 180 1126 0.000212 0.0152 0.000153 3.98e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 10/49 3.16G 0.1044 0.02972 0.0346 35 416: 100% 160/160 [00:26<00:00, 6.13it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.75it/s] all 180 1126 0.0002 0.0144 0.000139 3.92e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 11/49 3.16G 0.1026 0.02931 0.03401 12 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.79it/s] all 180 1126 0.000212 0.0152 0.000143 3.31e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 12/49 3.16G 0.1028 0.02886 0.03285 17 416: 100% 160/160 [00:26<00:00, 6.11it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.66it/s] all 180 1126 0.00023 0.0166 0.000165 4.16e-05 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 13/49 3.16G 0.1015 0.02814 0.0322 26 416: 100% 160/160 [00:26<00:00, 6.11it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.59it/s] all 180 1126 0.000925 0.0245 0.00053 0.000136 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 14/49 3.16G 0.101 0.02822 0.03288 25 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:14<00:00, 1.58it/s] all 180 1126 0.00191 0.00399 0.000528 0.000104 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 15/49 3.16G 0.1008 0.02822 0.03211 28 416: 100% 160/160 [00:26<00:00, 6.06it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:07<00:00, 3.11it/s] all 180 1126 0.124 0.00488 0.00287 0.000681 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 16/49 3.16G 0.09555 0.02773 0.0302 23 416: 100% 160/160 [00:26<00:00, 6.09it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.38it/s] all 180 1126 0.57 0.0114 0.00629 0.00156 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 17/49 3.16G 0.09376 0.02779 0.03108 34 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.13it/s] all 180 1126 0.577 0.0151 0.0105 0.00289 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 18/49 3.16G 0.09032 0.02834 0.03087 23 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.78it/s] all 180 1126 0.369 0.0307 0.0207 0.00554 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 19/49 3.16G 0.08742 0.02653 0.0306 14 416: 100% 160/160 [00:26<00:00, 6.09it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.20it/s] all 180 1126 0.583 0.0241 0.022 0.00649 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 20/49 3.16G 0.08427 0.02864 0.0291 52 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.26it/s] all 180 1126 0.701 0.0437 0.0336 0.0104 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 21/49 3.16G 0.08201 0.0278 0.02962 38 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.14it/s] all 180 1126 0.721 0.0549 0.0346 0.0101 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 22/49 3.16G 0.08073 0.02622 0.02915 24 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.20it/s] all 180 1126 0.723 0.0606 0.0382 0.0125 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 23/49 3.16G 0.07842 0.02557 0.02845 21 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.68it/s] all 180 1126 0.934 0.038 0.0392 0.0117 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 24/49 3.16G 0.07652 0.02457 0.02599 20 416: 100% 160/160 [00:26<00:00, 6.09it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.24it/s] all 180 1126 0.882 0.0471 0.0509 0.0162 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 25/49 3.16G 0.07589 0.02634 0.02656 44 416: 100% 160/160 [00:26<00:00, 6.04it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.05it/s] all 180 1126 0.869 0.0516 0.0532 0.0176 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 26/49 3.16G 0.07488 0.0248 0.02722 29 416: 100% 160/160 [00:26<00:00, 6.06it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.19it/s] all 180 1126 0.731 0.073 0.0592 0.0224 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 27/49 3.16G 0.07341 0.02631 0.02833 20 416: 100% 160/160 [00:26<00:00, 6.06it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.15it/s] all 180 1126 0.758 0.0603 0.0585 0.0235 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 28/49 3.16G 0.07101 0.0248 0.02688 16 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.20it/s] all 180 1126 0.736 0.0789 0.0687 0.0276 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 29/49 3.16G 0.07126 0.02508 0.02685 21 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.07it/s] all 180 1126 0.738 0.0818 0.0789 0.0333 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 30/49 3.16G 0.06962 0.02485 0.02663 10 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.17it/s] all 180 1126 0.748 0.0884 0.078 0.0347 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 31/49 3.16G 0.0686 0.02434 0.02711 14 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.03it/s] all 180 1126 0.743 0.0872 0.0902 0.0407 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 32/49 3.16G 0.06767 0.02472 0.02534 16 416: 100% 160/160 [00:26<00:00, 6.06it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.17it/s] all 180 1126 0.763 0.098 0.0839 0.0336 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 33/49 3.16G 0.06724 0.02471 0.02509 38 416: 100% 160/160 [00:26<00:00, 6.09it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.15it/s] all 180 1126 0.761 0.0997 0.103 0.0462 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 34/49 3.16G 0.06699 0.02491 0.02505 41 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.16it/s] all 180 1126 0.761 0.0932 0.0934 0.037 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 35/49 3.16G 0.06602 0.02311 0.0238 14 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.13it/s] all 180 1126 0.76 0.0982 0.0986 0.0366 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 36/49 3.16G 0.06521 0.02265 0.02395 35 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.24it/s] all 180 1126 0.767 0.103 0.115 0.056 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 37/49 3.16G 0.06453 0.02315 0.02497 22 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.17it/s] all 180 1126 0.796 0.0995 0.118 0.0558 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 38/49 3.16G 0.06377 0.02363 0.0235 37 416: 100% 160/160 [00:26<00:00, 6.05it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.22it/s] all 180 1126 0.699 0.11 0.114 0.0512 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 39/49 3.16G 0.06262 0.02458 0.02379 29 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.15it/s] all 180 1126 0.784 0.112 0.123 0.0532 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 40/49 3.16G 0.06218 0.02454 0.02325 27 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.23it/s] all 180 1126 0.8 0.122 0.13 0.0576 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 41/49 3.16G 0.06148 0.02278 0.02316 46 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.27it/s] all 180 1126 0.795 0.117 0.144 0.0696 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 42/49 3.16G 0.06057 0.02252 0.02142 12 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.22it/s] all 180 1126 0.769 0.121 0.179 0.0737 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 43/49 3.16G 0.06204 0.02443 0.02282 29 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.05it/s] all 180 1126 0.795 0.122 0.14 0.0697 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 44/49 3.16G 0.06016 0.02315 0.02329 9 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.97it/s] all 180 1126 0.814 0.12 0.138 0.0616 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 45/49 3.16G 0.05966 0.02336 0.02025 13 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.18it/s] all 180 1126 0.696 0.126 0.143 0.0657 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 46/49 3.16G 0.05842 0.02348 0.02148 37 416: 100% 160/160 [00:26<00:00, 6.08it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.25it/s] all 180 1126 0.814 0.118 0.147 0.0704 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 47/49 3.16G 0.05908 0.02319 0.02223 31 416: 100% 160/160 [00:26<00:00, 6.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.24it/s] all 180 1126 0.812 0.124 0.158 0.0765 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 48/49 3.16G 0.05855 0.02297 0.02077 10 416: 100% 160/160 [00:26<00:00, 6.07it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.03it/s] all 180 1126 0.818 0.126 0.161 0.0761 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 49/49 3.16G 0.05734 0.02385 0.02026 21 416: 100% 160/160 [00:26<00:00, 6.03it/s] Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 7.17it/s] all 180 1126 0.714 0.152 0.159 0.0736 50 epochs completed in 0.456 hours. Optimizer stripped from runs/train/exp3/weights/last.pt, 173.1MB Optimizer stripped from runs/train/exp3/weights/best.pt, 173.1MB Validating runs/train/exp3/weights/best.pt... Fusing layers... YOLOv5x summary: 322 layers, 86233975 parameters, 0 gradients, 203.9 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100% 23/23 [00:03<00:00, 6.09it/s] all 180 1126 0.812 0.124 0.158 0.0766 0- person 180 115 0.786 0.0087 0.0598 0.0208 1- bicycle 180 2 1 0 0 0 2- car 180 751 0.559 0.586 0.564 0.285 3- motorcycle 180 6 1 0 0.00404 0.00141 5- bus 180 6 1 0 0.187 0.126 6- train 180 8 1 0 0.0931 0.0432 7- truck 180 97 0.536 0.32 0.333 0.144 8- boat 180 1 1 0 0 0 9- traffic light 180 140 0.428 0.2 0.18 0.0676 Results saved to runs/train/exp3
In [5]:
p_yol5 = 0.812
r_yol5 = 0.124
mAP_yolo5 = 0.158
storeResults('MC-DAYolo',p_yol5,r_yol5,mAP_yolo5)
In [17]:
# dislaying metrics for train data
from IPython.display import Image
from IPython.display import display
x = Image(filename='runs/train/exp3/F1_curve.png')
y = Image(filename='runs/train/exp3/PR_curve.png')
z = Image(filename='runs/train/exp3/confusion_matrix.png')
display(x, y,z)
YoloV8
In [2]:
%cd /content
/content
In [3]:
!pip install ultralytics
Collecting ultralytics
Downloading ultralytics-8.0.229-py3-none-any.whl (663 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 663.2/663.2 kB 7.0 MB/s eta 0:00:00
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Collecting thop>=0.1.1 (from ultralytics)
Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)
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Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.2)
Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)
Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.1.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)
Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.3)
Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)
Installing collected packages: thop, ultralytics
Successfully installed thop-0.1.1.post2209072238 ultralytics-8.0.229
In [4]:
# Checking the size of images and displaying them
import numpy as np
import cv2
# Image shape in Training
image = cv2.imread('/content/drive/MyDrive/4/data/test/images/GOPR0475_frame_000300_rgb_anon_png.rf.bfa6bd4a07fa77a7f8112d6236838651.jpg')
height = np.size(image, 0)
width = np.size(image, 1)
print ("shape of the training image {}, {}".format(height, width))
# Image shape in validation
image = cv2.imread('/content/drive/MyDrive/4/data/valid/images/GOPR0475_frame_000148_rgb_anon_png.rf.5988210879733a56caea5a33b79c9259.jpg')
height = np.size(image, 0)
width = np.size(image, 1)
print ("shape of the validation image {}, {}".format(height, width))
shape of the training image 1280, 1280 shape of the validation image 1280, 1280
In [5]:
from ultralytics import YOLO
# Load a model
# model = YOLO("yolov8m.yaml") # build a new model from scratch
model = YOLO("yolov8m.pt") # load a pretrained model (recommended for training)
# Use the model
results = model.train(data="/content/drive/MyDrive/4/data/data.yaml", epochs=50, imgsz=415) # train the model
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to 'yolov8m.pt'...
100%|██████████| 49.7M/49.7M [00:00<00:00, 219MB/s]
Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
engine/trainer: task=detect, mode=train, model=yolov8m.pt, data=/content/drive/MyDrive/4/data/data.yaml, epochs=50, time=None, patience=50, batch=16, imgsz=415, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train
Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...
100%|██████████| 755k/755k [00:00<00:00, 17.3MB/s]
Overriding model.yaml nc=80 with nc=10
from n params module arguments
0 -1 1 1392 ultralytics.nn.modules.conv.Conv [3, 48, 3, 2]
1 -1 1 41664 ultralytics.nn.modules.conv.Conv [48, 96, 3, 2]
2 -1 2 111360 ultralytics.nn.modules.block.C2f [96, 96, 2, True]
3 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2]
4 -1 4 813312 ultralytics.nn.modules.block.C2f [192, 192, 4, True]
5 -1 1 664320 ultralytics.nn.modules.conv.Conv [192, 384, 3, 2]
6 -1 4 3248640 ultralytics.nn.modules.block.C2f [384, 384, 4, True]
7 -1 1 1991808 ultralytics.nn.modules.conv.Conv [384, 576, 3, 2]
8 -1 2 3985920 ultralytics.nn.modules.block.C2f [576, 576, 2, True]
9 -1 1 831168 ultralytics.nn.modules.block.SPPF [576, 576, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 2 1993728 ultralytics.nn.modules.block.C2f [960, 384, 2]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 2 517632 ultralytics.nn.modules.block.C2f [576, 192, 2]
16 -1 1 332160 ultralytics.nn.modules.conv.Conv [192, 192, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 2 1846272 ultralytics.nn.modules.block.C2f [576, 384, 2]
19 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2]
22 [15, 18, 21] 1 3781486 ultralytics.nn.modules.head.Detect [10, [192, 384, 576]]
Model summary: 295 layers, 25862110 parameters, 25862094 gradients, 79.1 GFLOPs
Transferred 469/475 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...
100%|██████████| 6.23M/6.23M [00:00<00:00, 86.3MB/s]
WARNING ⚠️ NMS time limit 0.550s exceeded
AMP: checks passed ✅
WARNING ⚠️ imgsz=[415] must be multiple of max stride 32, updating to [416]
train: Scanning /content/drive/MyDrive/4/data/train/labels... 639 images, 0 backgrounds, 0 corrupt: 100%|██████████| 639/639 [00:33<00:00, 18.89it/s]
train: New cache created: /content/drive/MyDrive/4/data/train/labels.cache albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
val: Scanning /content/drive/MyDrive/4/data/valid/labels... 180 images, 0 backgrounds, 0 corrupt: 100%|██████████| 180/180 [00:09<00:00, 19.46it/s]
val: New cache created: /content/drive/MyDrive/4/data/valid/labels.cache Plotting labels to runs/detect/train/labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.000714, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0) 50 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/50 3.17G 1.384 1.644 1.02 130 416: 100%|██████████| 40/40 [00:13<00:00, 2.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:02<00:00, 2.13it/s]
all 180 1126 0.637 0.301 0.32 0.183
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/50 3.32G 1.32 1.233 1.006 103 416: 100%|██████████| 40/40 [00:08<00:00, 4.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.79it/s]
all 180 1126 0.532 0.379 0.314 0.176
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/50 3.35G 1.389 1.285 1.033 105 416: 100%|██████████| 40/40 [00:08<00:00, 4.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.78it/s]
all 180 1126 0.426 0.299 0.304 0.144
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/50 3.36G 1.39 1.238 1.042 156 416: 100%|██████████| 40/40 [00:08<00:00, 4.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.78it/s]
all 180 1126 0.325 0.31 0.265 0.129
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/50 3.37G 1.4 1.178 1.036 113 416: 100%|██████████| 40/40 [00:08<00:00, 4.49it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.80it/s]
all 180 1126 0.524 0.268 0.296 0.152
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/50 3.34G 1.388 1.083 1.029 110 416: 100%|██████████| 40/40 [00:09<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.79it/s]
all 180 1126 0.359 0.269 0.294 0.145
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/50 3.37G 1.403 1.073 1.043 109 416: 100%|██████████| 40/40 [00:09<00:00, 4.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.84it/s]
all 180 1126 0.493 0.264 0.323 0.176
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/50 3.34G 1.357 1.073 1.021 64 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.85it/s]
all 180 1126 0.311 0.348 0.321 0.176
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/50 3.34G 1.341 1.055 1.031 102 416: 100%|██████████| 40/40 [00:09<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.71it/s]
all 180 1126 0.44 0.246 0.29 0.165
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/50 3.33G 1.328 0.9945 1.003 134 416: 100%|██████████| 40/40 [00:09<00:00, 4.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.63it/s]
all 180 1126 0.446 0.31 0.32 0.181
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/50 3.34G 1.305 0.9487 1.003 118 416: 100%|██████████| 40/40 [00:09<00:00, 4.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.67it/s]
all 180 1126 0.462 0.42 0.389 0.225
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/50 3.36G 1.311 0.9724 1.002 91 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.78it/s]
all 180 1126 0.448 0.452 0.397 0.224
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/50 3.36G 1.271 0.9184 0.9819 121 416: 100%|██████████| 40/40 [00:09<00:00, 4.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.83it/s]
all 180 1126 0.527 0.419 0.465 0.27
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/50 3.33G 1.234 0.8933 0.9817 154 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.85it/s]
all 180 1126 0.531 0.475 0.46 0.266
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/50 3.37G 1.216 0.8483 0.9697 117 416: 100%|██████████| 40/40 [00:09<00:00, 4.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.79it/s]
all 180 1126 0.587 0.461 0.442 0.244
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/50 3.34G 1.224 0.8664 0.9871 135 416: 100%|██████████| 40/40 [00:09<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.76it/s]
all 180 1126 0.403 0.415 0.423 0.25
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/50 3.36G 1.208 0.8341 0.9576 110 416: 100%|██████████| 40/40 [00:09<00:00, 4.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.66it/s]
all 180 1126 0.438 0.475 0.415 0.248
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/50 3.35G 1.184 0.8295 0.9535 75 416: 100%|██████████| 40/40 [00:09<00:00, 4.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.66it/s]
all 180 1126 0.475 0.468 0.436 0.258
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/50 3.34G 1.184 0.8278 0.9639 89 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.80it/s]
all 180 1126 0.484 0.496 0.466 0.281
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/50 3.36G 1.167 0.7979 0.9553 104 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.80it/s]
all 180 1126 0.508 0.471 0.453 0.272
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/50 3.36G 1.181 0.8114 0.9519 104 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.76it/s]
all 180 1126 0.532 0.487 0.53 0.32
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/50 3.36G 1.174 0.8005 0.9545 186 416: 100%|██████████| 40/40 [00:09<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.81it/s]
all 180 1126 0.464 0.411 0.45 0.256
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/50 3.34G 1.182 0.7875 0.9553 152 416: 100%|██████████| 40/40 [00:09<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.74it/s]
all 180 1126 0.461 0.417 0.456 0.277
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/50 3.33G 1.103 0.7386 0.9393 148 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.68it/s]
all 180 1126 0.481 0.385 0.446 0.251
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/50 3.35G 1.121 0.7596 0.9369 91 416: 100%|██████████| 40/40 [00:09<00:00, 4.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.68it/s]
all 180 1126 0.533 0.466 0.503 0.307
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/50 3.34G 1.108 0.7348 0.9281 62 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.80it/s]
all 180 1126 0.511 0.566 0.588 0.336
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/50 3.37G 1.097 0.7277 0.9303 116 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.78it/s]
all 180 1126 0.59 0.499 0.554 0.319
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/50 3.35G 1.09 0.7071 0.932 112 416: 100%|██████████| 40/40 [00:09<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.78it/s]
all 180 1126 0.588 0.537 0.549 0.332
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/50 3.34G 1.094 0.6918 0.9337 114 416: 100%|██████████| 40/40 [00:09<00:00, 4.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.71it/s]
all 180 1126 0.528 0.498 0.493 0.308
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/50 3.35G 1.108 0.7076 0.9316 115 416: 100%|██████████| 40/40 [00:09<00:00, 4.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.73it/s]
all 180 1126 0.528 0.468 0.536 0.352
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/50 3.36G 1.074 0.6798 0.9203 117 416: 100%|██████████| 40/40 [00:09<00:00, 4.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.68it/s]
all 180 1126 0.593 0.506 0.579 0.365
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/50 3.37G 1.062 0.6579 0.9166 151 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.70it/s]
all 180 1126 0.682 0.449 0.57 0.355
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/50 3.37G 1.049 0.6574 0.9134 89 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.61it/s]
all 180 1126 0.56 0.522 0.574 0.37
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/50 3.35G 1.065 0.6613 0.922 97 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.75it/s]
all 180 1126 0.487 0.534 0.545 0.345
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/50 3.36G 1.037 0.6435 0.9125 164 416: 100%|██████████| 40/40 [00:09<00:00, 4.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.83it/s]
all 180 1126 0.574 0.493 0.525 0.339
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/50 3.36G 1.04 0.641 0.9114 131 416: 100%|██████████| 40/40 [00:09<00:00, 4.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.81it/s]
all 180 1126 0.615 0.512 0.541 0.35
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/50 3.37G 1.021 0.6218 0.9095 126 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.86it/s]
all 180 1126 0.695 0.482 0.578 0.359
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/50 3.37G 1.002 0.6146 0.9 115 416: 100%|██████████| 40/40 [00:09<00:00, 4.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.76it/s]
all 180 1126 0.622 0.513 0.567 0.357
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/50 3.34G 0.9986 0.6141 0.8971 103 416: 100%|██████████| 40/40 [00:09<00:00, 4.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.69it/s]
all 180 1126 0.55 0.507 0.539 0.346
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/50 3.34G 0.997 0.6052 0.9093 122 416: 100%|██████████| 40/40 [00:09<00:00, 4.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.67it/s]
all 180 1126 0.583 0.505 0.57 0.366
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/50 3.33G 0.9937 0.5659 0.8935 111 416: 100%|██████████| 40/40 [00:11<00:00, 3.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.61it/s]
all 180 1126 0.541 0.551 0.598 0.382
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/50 3.34G 0.9733 0.5443 0.8906 83 416: 100%|██████████| 40/40 [00:09<00:00, 4.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.82it/s]
all 180 1126 0.655 0.527 0.612 0.385
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/50 3.35G 0.9862 0.5569 0.8828 86 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.77it/s]
all 180 1126 0.569 0.534 0.573 0.342
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/50 3.34G 0.9775 0.5506 0.8855 77 416: 100%|██████████| 40/40 [00:09<00:00, 4.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.75it/s]
all 180 1126 0.607 0.576 0.606 0.389
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/50 3.35G 0.9582 0.534 0.8875 97 416: 100%|██████████| 40/40 [00:09<00:00, 4.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.78it/s]
all 180 1126 0.662 0.513 0.607 0.384
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/50 3.35G 0.9505 0.5276 0.8851 73 416: 100%|██████████| 40/40 [00:09<00:00, 4.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.70it/s]
all 180 1126 0.647 0.555 0.618 0.394
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/50 3.34G 0.9427 0.5185 0.8801 93 416: 100%|██████████| 40/40 [00:09<00:00, 4.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.75it/s]
all 180 1126 0.61 0.591 0.625 0.39
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/50 3.33G 0.9376 0.5057 0.8809 97 416: 100%|██████████| 40/40 [00:09<00:00, 4.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.70it/s]
all 180 1126 0.59 0.582 0.634 0.398
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/50 3.34G 0.9156 0.5019 0.8768 98 416: 100%|██████████| 40/40 [00:09<00:00, 4.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.68it/s]
all 180 1126 0.615 0.586 0.637 0.399
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/50 3.35G 0.9174 0.4962 0.8708 128 416: 100%|██████████| 40/40 [00:09<00:00, 4.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:01<00:00, 3.76it/s]
all 180 1126 0.608 0.581 0.638 0.399 50 epochs completed in 0.172 hours. Optimizer stripped from runs/detect/train/weights/last.pt, 52.0MB Optimizer stripped from runs/detect/train/weights/best.pt, 52.0MB Validating runs/detect/train/weights/best.pt... Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) Model summary (fused): 218 layers, 25845550 parameters, 0 gradients, 78.7 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 6/6 [00:05<00:00, 1.07it/s]
all 180 1126 0.609 0.582 0.638 0.399
0- person 180 115 0.797 0.391 0.545 0.296
1- bicycle 180 2 0.186 0.5 0.578 0.339
2- car 180 751 0.841 0.792 0.862 0.566
3- motorcycle 180 6 0.616 0.667 0.681 0.354
5- bus 180 6 0.586 0.71 0.709 0.497
6- train 180 8 0.871 0.848 0.873 0.67
7- truck 180 97 0.731 0.73 0.774 0.524
8- boat 180 1 0 0 0 0
9- traffic light 180 140 0.856 0.596 0.717 0.35
Speed: 0.1ms preprocess, 4.7ms inference, 0.0ms loss, 0.8ms postprocess per image
Results saved to runs/detect/train
In [6]:
results = model.val() # evaluate model performance on the validation set
Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) Model summary (fused): 218 layers, 25845550 parameters, 0 gradients, 78.7 GFLOPs
val: Scanning /content/drive/MyDrive/4/data/valid/labels.cache... 180 images, 0 backgrounds, 0 corrupt: 100%|██████████| 180/180 [00:00<?, ?it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 12/12 [00:07<00:00, 1.60it/s]
all 180 1126 0.607 0.58 0.635 0.402
0- person 180 115 0.797 0.391 0.543 0.295
1- bicycle 180 2 0.187 0.5 0.578 0.339
2- car 180 751 0.84 0.792 0.861 0.569
3- motorcycle 180 6 0.617 0.667 0.681 0.354
5- bus 180 6 0.585 0.71 0.709 0.517
6- train 180 8 0.871 0.847 0.873 0.67
7- truck 180 97 0.729 0.72 0.766 0.525
8- boat 180 1 0 0 0 0
9- traffic light 180 140 0.837 0.589 0.704 0.351
Speed: 0.1ms preprocess, 10.0ms inference, 0.0ms loss, 2.8ms postprocess per image
Results saved to runs/detect/train2
In [6]:
p_yol5 = 0.607
r_yol5 = 0.58
mAP_yolo5 = 0.635
storeResults('YoloV8',p_yol5,r_yol5,mAP_yolo5)
In [7]:
# dislaying metrics for train data
from IPython.display import Image
from IPython.display import display
x = Image(filename='runs/detect/train/F1_curve.png')
y = Image(filename='runs/detect/train/PR_curve.png')
z = Image(filename='runs/detect/train/confusion_matrix.png')
display(x, y,z)
Comparison¶
In [7]:
#creating dataframe
import pandas as pd
result = pd.DataFrame({ 'ML Model' : ML_Model,
'Precision': precision,
'Recall' : recall,
'mAP' : mAP,
})
In [8]:
result
Out[8]:
| ML Model | Precision | Recall | mAP | |
|---|---|---|---|---|
| 0 | YoloV5x6 | 0.535 | 0.576 | 0.628 |
| 1 | YoloV4 | 0.799 | 0.187 | 0.256 |
| 2 | MC-DAYolo | 0.812 | 0.124 | 0.158 |
| 3 | YoloV8 | 0.607 | 0.580 | 0.635 |
Graph¶
In [9]:
import numpy as np
In [10]:
classifier = ML_Model
y_pos = np.arange(len(classifier))
Precision¶
In [12]:
import matplotlib.pyplot as plt2
plt2.barh(y_pos, precision, align='center', alpha=0.5,color='red')
plt2.yticks(y_pos, classifier)
plt2.xlabel('Precision Score')
plt2.title('Classification Performance')
plt2.show()
Recall¶
In [13]:
import matplotlib.pyplot as plt2
plt2.barh(y_pos, recall, align='center', alpha=0.5,color='yellow')
plt2.yticks(y_pos, classifier)
plt2.xlabel('Recall Score')
plt2.title('Classification Performance')
plt2.show()
mAP¶
In [14]:
import matplotlib.pyplot as plt2
plt2.barh(y_pos, mAP, align='center', alpha=0.5,color='navy')
plt2.yticks(y_pos, classifier)
plt2.xlabel('mAP')
plt2.title('Classification Performance')
plt2.show()
In [ ]: